Eliminate Privacy Concerns with Synthetic Data
config = LocalConfig(
epochs = 15,
gen_lines = 1000,
for line in generate_text(config):
Create safe data with synthetic datasets easily.
Generate synthetic datasets that retain the same insights and are statistically equivalent to your original data source.
Build privacy guarantees
into your existing workflows.
Create models with large amounts of artificial data that generalize better than those trained on limited datasets, with the added benefit of pretecting your customers’ privacy.
Improve limited datasets with synthetic data
Use synthetic data to augment data sources, improve accuracy, and reduce bias in machine learning models.Read the case study
Create synthetic data with privacy guarantees
Create and share realistic synthetic data freely across teams and organizations with differential privacy guaranteesRead the case study
Get started quickly with Gretel Blueprints
For more advanced usage, we have created a collection of Blueprints to help jumpstart your transformation workflows.
Synthetic data for everyone
Build and generate models that are mathematically guaranteed to be free of PII.
Train AI models on synthetic data without worrying about exposing PII or other sensitive data.
Anonymize precise customer data and share it safely across teams.
Make datasets publicly available so that engineers can monetize tools built for your data.
Train machine learning models
Generate synthetic data to augment your datasets. This can help you create AI and ML models that perform and generalize better, while reducing algorithmic bias.
Seamlessly share data
No need to snapshot production databases to share with your team. Define transformations to your data with software, and invite team members to subscribe to data feeds in real-time
Apply state of the art NLP processing to label personal data and PII in your data streams. Stay compliant by encrypting records containing unexpected PII in real-time.
Latest posts about Synthetic Data
Synthetic Data Configuration Templates
Our new configuration templates will help you pick some of the right parameters needed to train your synthetic data models.
Practical Privacy with Synthetic Data
Implementing a practical attack to measure un-intended memorization in synthetic data models.